Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models

• URL: http://arxiv.org/abs/2502.03715v1
• Date: Thu, 06 Feb 2025 02:06:48 GMT
• Title: Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
• Authors: Rui Cai, Chao Wang, Qianyi Cai, Dazhong Shen, Hui Xiong,
• Abstract summary: Large Language Models (LLMs) offer a promising way to improve the quality and relevance of Knowledge Graphs for recommendation tasks. We propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task. The framework includes: (1) an LLM-based subgraph augmenter for enriching KGs with high-quality information, (2) a confidence-aware message propagation mechanism to filter noisy triplets, and (3) a dual-view contrastive learning method to integrate user-item interactions and KG data.
• Score: 19.28217321004791
• License:
• Abstract: Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledge Graphs (KGs) is resource-intensive, and the accuracy of KGs can suffer from noisy, outdated, or irrelevant triplets. Recent advancements in Large Language Models (LLMs) offer a promising way to improve the quality and relevance of KGs for recommendation tasks. Despite this, integrating LLMs into KG-based systems presents challenges, such as efficiently augmenting KGs, addressing hallucinations, and developing effective joint learning methods. In this paper, we propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task. The framework includes: (1) an LLM-based subgraph augmenter for enriching KGs with high-quality information, (2) a confidence-aware message propagation mechanism to filter noisy triplets, and (3) a dual-view contrastive learning method to integrate user-item interactions and KG data. Additionally, we employ a confidence-aware explanation generation process to guide LLMs in producing realistic explanations for recommendations. Finally, extensive experiments demonstrate the effectiveness of CKG-LLMA across multiple public datasets.

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